Cooperative Relay Networks
In conventional relay networks, data packets are transmitted from a source node to a destination node via a single path with, perhaps, multiple serial hops through relay nodes.
In a cooperative relay networks, wireless nodes cooperate with each other in transmitting data packets in parallel. By exploiting the broadcast nature of a wireless channel to reach multiple relay nodes concurrently, and by enabling the relay nodes to cooperate, it is possible to reduce power consumption in delivering a packet from the source to the destination. This can also significantly increase gains in overall throughput and power efficiency, A. Nosratinia, T. Hunter and A. Hedayat, “Cooperative communication in wireless networks,” IEEE Communications Magazine, vol. 42, pp. 68-73, 2004; A. Sendonaris, E. Erkip and B. Aazhang, “User cooperation diversity-Part I: System description,” IEEE Transactions on Communications, vol. 51, pp. 1927-1938, 2003; A. Jardine, S. McLaughlin and J. Thompson, “Comparison of spacetime cooperative diversity relaying techniques,” in Proc. IEEE VTC 2005—Spring, pp. 2374-2378, 2005; J. N. Laneman, D. N. C. Tse, A. Stefanov and E. Erkip, “Cooperative coding for wireless networks,” IEEE Trans. Commun., pp. 1470-1476, September 2004; and G. W. Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” IEEE Transactions on Information Theory, vol. 50, pp. 3062-3080, 2004.
Cooperative relay network are useful for low cost, low power, low complexity sensor networks. If the nodes are powered by batteries, then minimizing power consumption is important. Minimizing total transmission time is one way to minimize power consumption.
Typically, the source node transmits data packets to the destination node in parallel using several intermediate relay nodes in parallel, A. Wittneben, I. Hammerstroem and M. Kuhn, “Joint cooperative diversity and scheduling in low mobility wireless networks,” IEEE Global Telecommunications Conference (GLOBECOM), vol. 2, pp. 780-784, 2004; A. E. Khandani, J. Abounadi, E. Modiano and L. Zheng, “Cooperative routing in wireless networks,” Allerton Conference on Communications, Control and Computing, 2003; and B. Rankov and A. Wittneben, “Distributed spatial multiplexing in a wireless network,” The Asilomar Conference on Signals, Systems, and Computers, pp. 1932-1937, 2004.
Cooperative beamforming is also referred to as distributed beamforming, see G. Barriac, R. Mudumbai and U. Madhow, “Distributed Beamforming for Information Transfer in Sensor Networks,” IPSN 2004, pp. 81-88, 2004. Mechanisms for enabling synchronization between relays using a trigger pulse mechanism from a master relay node were described. The effect of coordination error was analyzed. However, they do not take any relay selection or outage into account. The overall power consumption from the source to destination is also not considered.
Four simple relay selection criteria are described by J. Luo, R. S. Blum, L. J. Cimini, L. J. Greenstein and A. M. Haimovich, “Link-Failure Probabilities for Practical Cooperative Relay Networks,” IEEE Globecom 2005. Two of the criteria, ‘Pre-Select One Relay’ and ‘Best-Select Relay,’ select a single best relay based on a mean channel gain, while in the remaining two criteria, ‘Simple Relay’ and ‘ST-Coded Relay,’ all the relays that decode data from source are selected. In ‘Simple Relay,’ the relay nodes do not synchronize their phase, while in ‘ST-Coded Relay,’ a distributed space-time code is used. Hybrids of the above schemes were also described.
Search algorithms for selecting a single relay node based on an average distance or path loss between the nodes, a frame error probability, and a pairwise code word error probability were described in Z. Lin and E. Erkip “Relay Search Algorithms for Coded Cooperative Systems,” IEEE Globecom, 2005.
Khandani et al. describe a model that is restricted to additive white Gaussian noise (AWGN) channels with phase compensation. That model does not consider dynamic fading-induced channel variations, outage, or the overhead required for cooperation between relay nodes.
Knowledge of the channel state information (CSI) at a transmitter is assumed by Laneman et al. and Rankov et al. above. However, they do not consider the cost of acquiring the CSI. Wittneben et al. only considers amplify-and-forward, which also neglects the cost of acquiring the CSI, see also M. M. Abdallah and H. C. Papadopoulos, “Beamforming algorithms for decode-and-forward relaying in wireless networks,” Conference on Information Sciences and Systems, 2005.
If the relay nodes do not have the CSI, then the receiver can, at best, accumulate the mutual information from the various relay nodes, e.g., through space-time coding, see Luo et al. and Jardine et al. Outage analysis of such relay schemes, when the links operate at a given signal-to-noise ratio, are described by Y. Zhao, R. Adve and T. J. Lim, “Outage probability at arbitrary SNR with cooperative diversity,” IEEE Communications Letters, pp. 700-702, 2005; and A. Khisti, U. Erez, and G. Wornell, “Fundamental limits and scaling behavior of cooperative multicasting in wireless networks,” IEEE Trans. Information Theory, submitted 2006.
Mutual information accumulation is also assumed for a multicast and broadcast analysis described by I. Maric and R. D. Yates, “Cooperative multihop broadcast for wireless networks,” IEEE J. Selected Areas Comm., pp. 1080-1088, 2004; and I. Maric and R. D. Yates, “Cooperative multicast for maximum network lifetime,” IEEE J. Selected Areas Comm., pp. 127-135, 2005.
Rateless Codes
Rateless codes were developed to deal with communication scenarios where the receiver and transmitter do not know the channel statistics before transmitting. In such situations, using a standard block error-correcting code that is too weak for the channel noise, leads to unreliable communication, while using a block error-correcting code that is too strong for the channel noise is wasteful.
Rateless codes solve this problem. Instead of encoding the information bits to a pre-determined number of bits as in a standard block error-correcting code, the transmitter encodes the information bits into a potentially infinite stream of bits and starts transmitting the bits. After the receiver receives a sufficient number of bits, the receiver can decode the information bits, and the number of transmitted bits is efficient with respect to the channel.
Rateless codes have a long history in coding theory. For example, in 1974, Mandelbaum described puncturing low-rate block codes to build such a system, see D. M. Mandelbaum, “An Adaptive-Feedback Coding Scheme Using Incremental Redundancy,” IEEE Transactions on Information Theory, vol. 20, pp. 388-389, May 1974. Additional bits could be transmitted, at the request of the receiver, by sending bits that had previously been punctured. Mandelbaum used Reed-Solomon codes in his scheme, while others have investigated similar systems based on low-rate convolutional codes and turbo-codes, see J. Hagenauer, “Rate-compatible Punctured Convolutional Codes and their Applications,” IEEE Transactions on Communications, vol. 36, pp. 389-400, April 1998; and C. F. Leanderson, G. Caire and O. Edfors, “On the Performance of Incremental Redundancy Schemes with Turbo Codes,” Proc. Radiovetenskap och Kommunikation, pp. 57-61, June 2002.
Rateless codes were originally designed for erasure channels, but their performance for AWGN channels is good, see O. Etasami, M. Molkaraie and A. Shokrollahi, “Raptor codes on symmetric channels,” in Proc. Int. Symp. Information Theory, p. 38, 2004; and R. Palanki and J. Yedidia, “Rateless codes on noisy channels,” in Proc. Int. Symp. Information Theory, p. 37, 2004.
Rateless codes have also been used for wired Ethernet-like applications, as well as for point-to-point and broadcast and multicast applications in wireless networks, J. Castura and Y. Mao, “Rateless coding over fading channels,” IEEE Communications Letters, vol. 10, pp. 46-48, 2006; and H. Jenkac and T. Stockhammer, “Asynchronous media streaming over wireless broadcast channels,” in IEEE Int. Conf. Multimedia and Expo 2005, pp. 1318-1321, 2005.